Feature based passive acoustic detection of underwater threats

Sutin, Alexander; Radhakrishnan, Sreeram; Bruno, Michael S.; Fullerton, Brian; Ekimov, Alexander; Raftery, Michael

doi:10.1117/12.663651

Cited by 35 publications

(32 citation statements)

References 3 publications

(3 reference statements)

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“…Based on previous understanding [8], [9], the abilities to detect underwater threats, continue to be the issues of growing interest with the increasing concerns of detrimental effects on the marine environment. The method of passive acoustic detection for security threats is still a significant topic for further research and development.…”

Section: Motivationmentioning

confidence: 99%

Passive acoustic detection of a small remotely operated vehicle

Cai

Bingham

2011

OCEANS 2011 IEEE - Spain

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This project quantifies the feasibility of passive acoustic detection of a small commercial off-the-shelf (COTS) remotely operated vehicle (ROV) by experimentally characterizing the acoustic signature and hydrodynamic flow associated with the motion of ROV.Three experiments are conducted as parts of this characterization in this project.These trials are conducted in a small laboratory test tank, a larger outdoor test tank and a shallow-water coastal ocean environment. The laboratory tank test consists of a stationary hydrophone to detect the acoustic emissions, while simultaneously measuring the flow field using particle image velocimetry (PIV) system. Based on a variety of maneuvers in the test tank, the experiments are quantified by the acoustic signatures of the ROV. Also, we predict the capabilities of acoustically detecting an ROV in typical ocean environments such as shallow-water, ports and harbors, and deep-ocean environment. In addition, this thesis reports the results of a series of underwater detection trials in the field to verify the laboratory demonstration and analytical moderns. The intention is to experimentally measure the signal-to-noise ratio (SNR) of the ROV acoustic signature, as a function of distance between the ROV and acoustic receivers in the shallow-water ocean environment.On the basis of experimental analysis, we present that the most significant contribution for ROV acoustic detection is from electric motors with signal frequency range of around 400-500 Hz. The detectable distance range with ambient noise at 116 dB re 1 uPa is between 15 m and 22 m away in a noisy littoral environment. Combined with the experimental analysis, we state the comparison between the acoustic predicted levels of the ROV with the measured results.Finally, this project demonstrates the feasibilities of acoustical detection of a small ROV in the port environment. As a result, we conclude the experimental studies with later highlights and future research.iii DEDICATION To my dearest dad, mom and the whole familyWho love me and support me unconditionally iv ACKNOWLEDGMENTS

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Section: Motivationmentioning

confidence: 99%

Passive acoustic detection of a small remotely operated vehicle

Cai

Bingham

2011

OCEANS 2011 IEEE - Spain

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“…Optimization of a sensor network for threat detection embraces a wide range of problems, including optimal sensor placement [8,23,27], development of detection algorithms and data aggregation techniques [19,23,25,28], threat tracking [1][2][3]14], efficient resource allocation (e.g., sensor battery power [11,20], communication bandwidth [10,24], etc.) and optimal control strategies for networks with mobile and steerable sensors, to mention just a few.…”

Section: Introductionmentioning

confidence: 99%

Optimization of steerable sensor network for threat detection

Molyboha

Zabarankin

2011

Naval Research Logistics

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A Markov chain approach to detecting a threat in a given surveillance zone by a network of steerable sensors is presented. The network has a finite number of predetermined states, and transition from one state to another follows a Markov chain. Under the assumption that the threat avoids detection, two game theoretic problems for finding an optimal Markov chain (two surveillance strategies) are formulated: the first maximizes the probability of threat detection for two consecutive detection periods, whereas the second minimizes the average time of detection for the worst-case threat's trajectory. Both problems are reduced to linear programming, and special techniques are suggested to solve them. For a dynamic environment with moving noise sources, the optimal Markov chain changes at each detection period, and the rate of convergence of the Markov chain to its stationary distribution is analyzed. Both surveillance strategies are tested in numerical experiments and compared one with another.

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“…Assuming that the performance of each sensor is independent of the others, it is easy to show that the total probability that a diver, crossing at point , be detected by the entire set of sensors is given by (1) The terms, , are calculated using a simple sensor model in which probability of detection is approximated by a linear decrease with range from a maximum value of 95% to reflect the fact that detection is never guaranteed. This model is based on recent work on passive diver detection [6], which suggests that divers can be detected by thresholding a feature value derived from a passive acoustic hydrophone signal. Note that the technique described here can accommodate any detection versus range sensor model.…”

Section: Probability Of Detectionmentioning

confidence: 99%

Using Environmental Models to Optimize Sensor Placement

Vickers

Nickerson

2007

IEEE Sensors J.

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Abstract-Sensors used in protective applications are conventionally placed on perimeters or over areas in an evenly distributed pattern. However, such patterns may actually be suboptimal, since environmental factors may make some forms of attack more or less likely than others. We describe a protective application of sensors for detecting underwater threats in an urban estuary environment. We demonstrate that environmental information, derived from a computational river current model, can be utilized to optimize sensor placement, increasing detection rates and decreasing the number of required sensors. Simulation results show a significant improvement in detection likelihood for a given number of sensors; alternatively, fewer sensors can be used while still maintaining the detection rate of a conventional approach.

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Feature based passive acoustic detection of underwater threats

Cited by 35 publications

References 3 publications

Passive acoustic detection of a small remotely operated vehicle

Passive acoustic detection of a small remotely operated vehicle

Optimization of steerable sensor network for threat detection

Using Environmental Models to Optimize Sensor Placement

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